长汀红壤侵蚀区马尾松林生物量估算模型的构建
|
摘要
以长汀红壤侵蚀区马尾松为研究对象,通过整株收获法获取34株马尾松立木材积和生物量,分析不同龄级、径级马尾松材积和生物量分配格局,采用胸径(D)、树高(H)等变量建立立木材积模型,采用材积量(V)、胸径(D)、树高(H)、冠长(C_l)等变量建立树干、树冠及地上生物量模型,进而拟合区域林分生物量模型,使用独立样本检验并比较优选模型估测效果。结果表明:34株马尾松的树龄变化范围为19～42 a,立木材积量和立木生物量变化范围分别为0.004 4～0.194 9、2.733 9～140.331 4 kg/株,树龄与材积量、生物量相关性不显著;各器官生物量分配为干材(57.67±8.28)%、树枝(24.15±7.33)%、树叶(10.79±3.17)%、干皮(7.38±1.39)%,全林分中3个径阶(8、10、12 cm)蓄积量、生物量均超过总量的50%;所有模型确定系数均大于93%,单木模型中,以胸径-树高组合为自变量的模型拟合效果更佳;马尾松立木材积、地上生物量、树干生物量、树冠生物量及林分生物量模型中,各优选模型预估精度均达77%以上,其中立木材积、地上生物量及林分生物量优选模型比已有模型估测值的总相对误差、平均相对误差均有所降低,估测值更接近实际值。因此,通过构建该区域马尾松生物量方程,补充了长汀红壤侵蚀区马尾松立木材积表及生物量表。
With Pinus massoniana in Red Soil Erosion Area, Changting County of Fujian Province, the volume and biomass of P. massoniana was analyzed by using the whole-tree harvesting method. First, we analyzed the relationship between age, DBH and volume, and compared volume and biomass allocation patterns of different organs at different age and DBH classes. Second, we selected appropriate independent variables to develop allometric equations. We selected the DBH and tree height as independent variables to establish single volume model, selected the volome, DBH, tree height, crown length and other independent variables to establish the trunk biomass model, the crown biomass model and single wood biomass model, and fitted the regional forest biomass estimation model. At last, we used independent sample to test the errors and accuracy of the optimal model, and compared the estimation results of the general model for volume and biomass of P. massoniana in Fujian Province. The age range of 34 Pinus massoniana varies from 19 to 42 a, average single wood volume varies from 0.004 4 to 0.194 9 m~3, above-ground single wood biomass varies from 2.733 9 to 140.331 4 kg, and there is no significant correlation between tree age and volume and biomass. The biomass allocation ratio of each organ was stem without bark biomass(57.67±8.28)%, branch biomass(24.15±7.33)%, leaf biomass(10.79±3.17)%, and bark biomass(7.38±1.39)%. The volume and biomass of the three DBH classes(8, 10, and 12 cm) in the whole forest exceeded 50% of the total amount. R~2 for all models is greater than 93%, DBH-tree height combined variable can improve the fitting effect of single volume and biomass model. The optimal Single wood volume mode, Single wood biomass model, Trunk biomass model, Crown biomass model, Forest biomass model have an accuracy of more than 77%, the total relative error and average relative error of optimized single wood volume model, single wood biomass model, and forest biomass model are lower than those general model. The prediction accuracy of trunk and crown biomass models is relatively low. Compared with existing models, the estimated value is closer to the actual value. Constructing the allometric equation for P. massoniana supplemented single wood volume and biomass table in eroded area of Changting red soil.
With Pinus massoniana in Red Soil Erosion Area, Changting County of Fujian Province, the volume and biomass of P. massoniana was analyzed by using the whole-tree harvesting method. First, we analyzed the relationship between age, DBH and volume, and compared volume and biomass allocation patterns of different organs at different age and DBH classes. Second, we selected appropriate independent variables to develop allometric equations. We selected the DBH and tree height as independent variables to establish single volume model, selected the volome, DBH, tree height, crown length and other independent variables to establish the trunk biomass model, the crown biomass model and single wood biomass model, and fitted the regional forest biomass estimation model. At last, we used independent sample to test the errors and accuracy of the optimal model, and compared the estimation results of the general model for volume and biomass of P. massoniana in Fujian Province. The age range of 34 Pinus massoniana varies from 19 to 42 a, average single wood volume varies from 0.004 4 to 0.194 9 m~3, above-ground single wood biomass varies from 2.733 9 to 140.331 4 kg, and there is no significant correlation between tree age and volume and biomass. The biomass allocation ratio of each organ was stem without bark biomass(57.67±8.28)%, branch biomass(24.15±7.33)%, leaf biomass(10.79±3.17)%, and bark biomass(7.38±1.39)%. The volume and biomass of the three DBH classes(8, 10, and 12 cm) in the whole forest exceeded 50% of the total amount. R~2 for all models is greater than 93%, DBH-tree height combined variable can improve the fitting effect of single volume and biomass model. The optimal Single wood volume mode, Single wood biomass model, Trunk biomass model, Crown biomass model, Forest biomass model have an accuracy of more than 77%, the total relative error and average relative error of optimized single wood volume model, single wood biomass model, and forest biomass model are lower than those general model. The prediction accuracy of trunk and crown biomass models is relatively low. Compared with existing models, the estimated value is closer to the actual value. Constructing the allometric equation for P. massoniana supplemented single wood volume and biomass table in eroded area of Changting red soil.
引文
[1] 董利虎,李凤日,贾炜玮.基于线性混合效应的红松人工林枝条生物量模型[J].应用生态学报,2013,24(12):3391-3398.
[2] 杨光.燕儿沟流域植被土壤水分生物量及生物多样性研究[D].杨凌:西北农林科技大学,2009.
[3] 何海,乔永康,刘庆,等.亚高叶针叶林人工恢复过程中生物量和材积动态研究[J].应用生态学报,2004,15(5):748-752.
[4] 马志阳,查轩.南方红壤区侵蚀退化马尾松林地生态恢复研究[J].水土保持研究,2008,15(3):188-193.
[5] 姜春武,徐庆,张蓓蓓,等.马尾松光合生理特性及资源利用效率研究进展[J].世界林业研究,2017,30(4):24-28.
[6] 陈樟昊,姚雄,余坤勇,等.南方典型红壤区生态脆弱性与土壤侵蚀的演化关系[J].西南林业大学学报,2017,37(4):82-90.
[7] 黄绍霖,徐涵秋,林娜,等.亚热带地区马尾松林碳储量的遥感估算:以长汀河田盆地为例[J].生态学报,2013,33(10):2992-3001.
[8] 黄绍霖,徐涵秋,曾宏达,等.福建长汀河田盆地的马尾松林碳储量时空动态变化[J].地球科学(中国地质大学学报),2013,38(5):1081-1090.
[9] 陈淑桂.水土流失治理区马尾松林生物量遥感研究[D].福州:福建师范大学,2011.
[10] 曾伟生,王雪军,陈振雄,等.林分起源对立木生物量模型的影响分析[J].林业资源管理,2014(2):40-45.
[11] 赵菡,雷渊才,符利勇.江西省不同立地等级的马尾松林生物量估计和不确定性度量[J].林业科学,2017,53(8):81-93.
[12] 韩畅,宋敏,杜虎,等.广西不同林龄杉木、马尾松人工林根系生物量及碳储量特征[J].生态学报,2017,37(7):2282-2289.
[13] 曾伟生.全国立木生物量方程建模方法研究[D].北京:中国林业科学研究院,2011.
[14] 曾伟生,肖前辉,胡觉,等.中国南方马尾松立木生物量模型研建[J].中南林业科技大学学报,2010,30(5):50-56.
[15] 刘琪璟.中国立木材积表[M].北京:中国林业出版社,2017.
[16] 国家林业局.立木生物量模型及碳计量参数:马尾松:LY/T 2263-2014[S].北京:中国标准出版社,2014.
[17] 陈昌雄,余坤勇,杨子清,等.相容性马尾松林分生物量估测模型的构建研究[J].三明学院学报,2010,27(4):379-382.
[18] 方精云,陈安平,赵淑清,等.中国森林生物量的估算:对Fang等Science一文(Science,2001,291:2320～2322)的若干说明[J].植物生态学报,2002,26(2):243-249.
[19] 董利虎,李凤日,宋玉文.东北林区4个天然针叶树种单木生物量模型误差结构及可加性模型[J].应用生态学报,2015,26(3):704-714.
[20] 潘萍,韩天一,欧阳勋志,等.飞播马尾松林碳密度分配特征及其影响因素[J].应用生态学报,2017,28(12):3841-3847.
[21] 杜虎,宋同清,曾馥平,等.桂东不同林龄马尾松人工林的生物量及其分配特征[J].西北植物学报,2013,33(2):394-400.
[22] 方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产量[J].生态学报,1996,16(5):497-508.
[23] 周祎,丁贵杰.贵州省马尾松人工林生物量及其分布格局研究[J].贵州林业科技,2016,44(2):1-7.
[24] 姚雄,余坤勇,刘健,等.南方水土流失严重区的生态脆弱性时空演变[J].应用生态学报,2016,27(3):735-745.
[25] MOKANY K,RAISON R J,PROKUSHKIN A S.Critical analysis of root:shoot ratios in terrestrial biomass[J].Global Change Biology,2006,12:84-96.doi:10.1111/j.1365-2486.2005.001043.x.
[26] SCHMID I.The influence of soil type and interspecific competition on the fine root system of Norway spruce and European beech[J].Basic and Applied Ecology,2002,3(4):339-346.doi:10.1078/1439-1791-00116.
[27] LIM H,LEE K H,LEE K H,et al.Biomass expansion factors and allometric equations in an age sequence for Japanese cedar (Cryptomeria japonica) in southern Korea[J].Journal of Forest Research,2013,18(4):316-322.
[28] 汪金松,张春雨,范秀华,等.臭冷杉生物量分配格局及异速生长模型[J].生态学报,2011,31(14):3918-3927.
[29] 丁贵杰.马尾松人工林生物量和生产力研究(Ⅰ):不同造林密度生物量及密度效应[J].福建林学院学报,2003,23(1):34-38.
[30] 吕勇,曾思齐.马尾松林分生物量的研究[J].中南林学院学报,1996,16(4):28-32.
[31] 王天博.福建将乐马尾松人工林单木生物量模型研究[D].北京:北京林业大学,2012.
[32] PARRESOL B R.Assessing tree and stand biomass:a review withexamples and critical comparisons[J].Forest Science,1999,45(4):573-593.
[33] PEICGL M,ARAIN M A.Allometry and partitioning of above-and belowground tree biomass in an age-sequence of wliite pine forests[J].Forest Ecology and Management,2007,253(1/2/3):68-80.
[34] 刘坤,曹林,汪贵斌,等.银杏生物量分配格局及异速生长模型[J].北京林业大学学报,2017,39(4):12-20.
[35] MACDICKN K G.A guide to monitoring carbon storage in forestry and agroforestry projects[R].Winrock:Winrock International Institute for Agricultural Development,1997.
[36] 贺东北,骆期邦,曾伟生.立木生物量线性联立模型研究[J].浙江林学院学报,1998,15(3):298-303.
[37] TER-MIKAELIAN M T,KORZUKHIN M D.Biomass equations for sixty-five North American tree species[J].Forest Ecology and Management,1997,97(1):1-24.
[38] 林力.马尾松人工林生物量模型的研究[D].福州:福建农林大学,2011.
[39] HOCHBICHLER E,BELLOS P,LICK E.Biomass functions for estimating needle and branch biomass of spruce (Picea Abies) and Scots pine (Pinus Sylvestries) and branch biomass of beech (Fagus sylvatica) and oak (Quercus robur and petrea)[J].Austrian Journal of Forest Science,2006,123(1/2):35-46.
[40] 李海奎,宁金魁.基于树木起源、立地分级和龄组的单木生物量模型[J].生态学报,2012,32(3):740-757.
[41] BALDWIN V C,PETERSON K D,BURKHART H E,et al.Equations for estimating loblolly pine branch and foliage weight and surface area distributions[J].Canadian Journal of Forest Research,1997,27(6):918-927.
[42] XIAO C W,CEULEMANS R.Allometric relationships for below-and aboveground biomass of young Scots pines[J].Forest Ecology and Management,2004,203(1/2/3):177-186.
[2] 杨光.燕儿沟流域植被土壤水分生物量及生物多样性研究[D].杨凌:西北农林科技大学,2009.
[3] 何海,乔永康,刘庆,等.亚高叶针叶林人工恢复过程中生物量和材积动态研究[J].应用生态学报,2004,15(5):748-752.
[4] 马志阳,查轩.南方红壤区侵蚀退化马尾松林地生态恢复研究[J].水土保持研究,2008,15(3):188-193.
[5] 姜春武,徐庆,张蓓蓓,等.马尾松光合生理特性及资源利用效率研究进展[J].世界林业研究,2017,30(4):24-28.
[6] 陈樟昊,姚雄,余坤勇,等.南方典型红壤区生态脆弱性与土壤侵蚀的演化关系[J].西南林业大学学报,2017,37(4):82-90.
[7] 黄绍霖,徐涵秋,林娜,等.亚热带地区马尾松林碳储量的遥感估算:以长汀河田盆地为例[J].生态学报,2013,33(10):2992-3001.
[8] 黄绍霖,徐涵秋,曾宏达,等.福建长汀河田盆地的马尾松林碳储量时空动态变化[J].地球科学(中国地质大学学报),2013,38(5):1081-1090.
[9] 陈淑桂.水土流失治理区马尾松林生物量遥感研究[D].福州:福建师范大学,2011.
[10] 曾伟生,王雪军,陈振雄,等.林分起源对立木生物量模型的影响分析[J].林业资源管理,2014(2):40-45.
[11] 赵菡,雷渊才,符利勇.江西省不同立地等级的马尾松林生物量估计和不确定性度量[J].林业科学,2017,53(8):81-93.
[12] 韩畅,宋敏,杜虎,等.广西不同林龄杉木、马尾松人工林根系生物量及碳储量特征[J].生态学报,2017,37(7):2282-2289.
[13] 曾伟生.全国立木生物量方程建模方法研究[D].北京:中国林业科学研究院,2011.
[14] 曾伟生,肖前辉,胡觉,等.中国南方马尾松立木生物量模型研建[J].中南林业科技大学学报,2010,30(5):50-56.
[15] 刘琪璟.中国立木材积表[M].北京:中国林业出版社,2017.
[16] 国家林业局.立木生物量模型及碳计量参数:马尾松:LY/T 2263-2014[S].北京:中国标准出版社,2014.
[17] 陈昌雄,余坤勇,杨子清,等.相容性马尾松林分生物量估测模型的构建研究[J].三明学院学报,2010,27(4):379-382.
[18] 方精云,陈安平,赵淑清,等.中国森林生物量的估算:对Fang等Science一文(Science,2001,291:2320～2322)的若干说明[J].植物生态学报,2002,26(2):243-249.
[19] 董利虎,李凤日,宋玉文.东北林区4个天然针叶树种单木生物量模型误差结构及可加性模型[J].应用生态学报,2015,26(3):704-714.
[20] 潘萍,韩天一,欧阳勋志,等.飞播马尾松林碳密度分配特征及其影响因素[J].应用生态学报,2017,28(12):3841-3847.
[21] 杜虎,宋同清,曾馥平,等.桂东不同林龄马尾松人工林的生物量及其分配特征[J].西北植物学报,2013,33(2):394-400.
[22] 方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产量[J].生态学报,1996,16(5):497-508.
[23] 周祎,丁贵杰.贵州省马尾松人工林生物量及其分布格局研究[J].贵州林业科技,2016,44(2):1-7.
[24] 姚雄,余坤勇,刘健,等.南方水土流失严重区的生态脆弱性时空演变[J].应用生态学报,2016,27(3):735-745.
[25] MOKANY K,RAISON R J,PROKUSHKIN A S.Critical analysis of root:shoot ratios in terrestrial biomass[J].Global Change Biology,2006,12:84-96.doi:10.1111/j.1365-2486.2005.001043.x.
[26] SCHMID I.The influence of soil type and interspecific competition on the fine root system of Norway spruce and European beech[J].Basic and Applied Ecology,2002,3(4):339-346.doi:10.1078/1439-1791-00116.
[27] LIM H,LEE K H,LEE K H,et al.Biomass expansion factors and allometric equations in an age sequence for Japanese cedar (Cryptomeria japonica) in southern Korea[J].Journal of Forest Research,2013,18(4):316-322.
[28] 汪金松,张春雨,范秀华,等.臭冷杉生物量分配格局及异速生长模型[J].生态学报,2011,31(14):3918-3927.
[29] 丁贵杰.马尾松人工林生物量和生产力研究(Ⅰ):不同造林密度生物量及密度效应[J].福建林学院学报,2003,23(1):34-38.
[30] 吕勇,曾思齐.马尾松林分生物量的研究[J].中南林学院学报,1996,16(4):28-32.
[31] 王天博.福建将乐马尾松人工林单木生物量模型研究[D].北京:北京林业大学,2012.
[32] PARRESOL B R.Assessing tree and stand biomass:a review withexamples and critical comparisons[J].Forest Science,1999,45(4):573-593.
[33] PEICGL M,ARAIN M A.Allometry and partitioning of above-and belowground tree biomass in an age-sequence of wliite pine forests[J].Forest Ecology and Management,2007,253(1/2/3):68-80.
[34] 刘坤,曹林,汪贵斌,等.银杏生物量分配格局及异速生长模型[J].北京林业大学学报,2017,39(4):12-20.
[35] MACDICKN K G.A guide to monitoring carbon storage in forestry and agroforestry projects[R].Winrock:Winrock International Institute for Agricultural Development,1997.
[36] 贺东北,骆期邦,曾伟生.立木生物量线性联立模型研究[J].浙江林学院学报,1998,15(3):298-303.
[37] TER-MIKAELIAN M T,KORZUKHIN M D.Biomass equations for sixty-five North American tree species[J].Forest Ecology and Management,1997,97(1):1-24.
[38] 林力.马尾松人工林生物量模型的研究[D].福州:福建农林大学,2011.
[39] HOCHBICHLER E,BELLOS P,LICK E.Biomass functions for estimating needle and branch biomass of spruce (Picea Abies) and Scots pine (Pinus Sylvestries) and branch biomass of beech (Fagus sylvatica) and oak (Quercus robur and petrea)[J].Austrian Journal of Forest Science,2006,123(1/2):35-46.
[40] 李海奎,宁金魁.基于树木起源、立地分级和龄组的单木生物量模型[J].生态学报,2012,32(3):740-757.
[41] BALDWIN V C,PETERSON K D,BURKHART H E,et al.Equations for estimating loblolly pine branch and foliage weight and surface area distributions[J].Canadian Journal of Forest Research,1997,27(6):918-927.
[42] XIAO C W,CEULEMANS R.Allometric relationships for below-and aboveground biomass of young Scots pines[J].Forest Ecology and Management,2004,203(1/2/3):177-186.